In image registration, an optimal transformation is sought between different image acquisitions of one or more objects. Image registration techniques may be used in the medical field to relate a pre-operative image of a patient's anatomy to a near real-time image of the patient during actual treatment. During radiosurgery, for example, the change in target position at the time of treatment, as compared to its position at the time of the diagnostic treatment planning, may be detected. This may be accomplished by registering the 2D image acquired at treatment time with the 3D scan obtained at the time of treatment planning. A robust and accurate 2D-3D image registration algorithm may enable the position of the target, as viewed in the real-time 2D image, to be properly correlated with the pre-operative 3D scan. In practice, a formal mathematical transformation may be derived that best aligns the pre-operative image coordinate system with the patient's physical world coordinate system, defined for example in the treatment room.
Fiducial markers may be attached to, or implanted in, the patient before the pre-operative images are acquired, in order to accomplish a point-based alignment of the different coordinate systems. These fiducial markers may be designed so that they can be localized accurately in the pre-operative image as well as in the real physical world. The respective localization points may then be used to calculate a rigid body transformation between the two coordinate systems.
Fiducials-based tracking can be difficult for the patient, however, for a number of reasons. For example, high accuracy tends to be achieved by using bone-implanted fiducial markers, but less invasive techniques such as skin-attached markers or anatomical positions tend to be less accurate. Implantation of fiducials into a patient may be painful and difficult, especially for the C-spine, the implantation process for which may frequently lead to clinical complications. Attempts have therefore been made to develop techniques for fiducial-less tracking.
Some known methods of fiducial-less tracking may assume a rigid body transformation, i.e. a rigid body rotation and a rigid body translation. Such a rigid transformation may ignore local variations during the transformation, and assume that the patient's anatomy is a rigid body, and that all the rigid body constraints should be preserved. Various clinical data have shown that such a rigid transformation model may be inadequate in many cases. For example, although the deformation of an individual bone may be relatively accurately modeled as a rigid transformation, adjacent bones could move relatively to the individual bone, and soft tissue surrounding the bones could deform. Accordingly, non-rigid registration algorithms may permit real patient body deformation to be accounted for, and may permit an anatomical region to be tracked more precisely.
In some forms of non-rigid image registration, non-rigid deformation fields may be defined that account for local anatomical variations found in the different image acquisitions. To derive the non-rigid deformation field, a full motion field may be computed that is composed of many local motion vectors.
It is desirable to reliably and efficiently estimate these local motion vectors, and to accurately derive the full motion field from these locally estimated motion vectors.